Crowdsourced 3D cadastral surveys: looking towards the next 10 years

  • Maria GkeliEmail author
  • Chryssy Potsiou
  • Charalabos Ioannidis
Original Article


Rapidly growing cities, multiple uses of urban space and the complexity of overlapping property rights require various types of rights to be registered and handled in a uniform and reliable way, considering the third dimension. The adoption of automated and low-cost but reliable procedures for cadastral surveys and for the capture and processing of cadastral data, as well as the use of modern Information Technology (IT) tools and m-services, is the beginning of a new cadastral evolution. 3D-crowdsourced cadastral data capture has huge potential and may soon facilitate the work of National Mapping Agencies (NMAs). In this paper, an innovative fit-for-purpose procedure is designed and initially tested that aims to save time and costs and to provide a modern technical solution for the initial collection, registration and visualization of 3D cadastral data. An open-source, mobile application for the acquisition of 3D crowdsourced cadastral data and 3D modelling and visualization of property units is developed, tested and presented. The proposed technical procedure is adjustable and may be used in both the developed and the developing world. The geometric accuracy of the final product depends on the geometric accuracy of the basemaps used. The developed application is tested on a multi-story building in an urban area of Larisa, in Greece. An initial evaluation of the procedure and the final product, in terms of its usability, affordability, reliability and implementation duration, is conducted. The first results are satisfactory and may lead to a fit-for-purpose procedure for a 3D cadastre for all in the future.


3D cadastre Crowdsourcing 3D modelling Land administration 

JEL classification

O43 O47 O17 P14 P25 P26 P48 Q15 R31 R38 R52 F3 



The contribution of Maria Gkeli to this research is part of her PhD dissertation, which is supported by the Onassis Foundation scholarship program.


  1. Ammar RK, Neeraj D (2013) SLRB Bahrain—3D property registration system. In: 5th Land administration domain model workshop, Kuala Lumpur, pp 419–432Google Scholar
  2. Apostolopoulos K, Geli M, Petrelli P, Potsiou C, Ioannidis C (2016) A new model for cadastral surveying using crowdsourcing. Surv Rev 50(359):122–133. CrossRefGoogle Scholar
  3. Basiouka S, Potsiou C (2012a) VGI in cadastre—a greek experiment to investigate the potential of crowd sourcing techniques in cadastral mapping. Surv Rev 44(325):153–161CrossRefGoogle Scholar
  4. Basiouka S, Potsiou C (2012b) Improving cadastral survey procedures using crowdsourcing techniques. Coord Mag VIII(10):20–26Google Scholar
  5. Basiouka S, Potsiou C (2013) The volunteered geographic information in cadastre: perspectives and citizens’ motivations over potential participation in mapping. GeoJournal 79(3):343–355CrossRefGoogle Scholar
  6. Basiouka S, Potsiou C, Bakogiannis E (2015) OpenStreetMap for cadastral purposes: an application using VGI for official processes in urban areas. Surv Rev 47(344):333–341CrossRefGoogle Scholar
  7. Benhamu M (2006) A GIS-related multi layers 3D cadastre in Israel. In: XXIII FIG congress—shaping the change, Munich, pp 1–12Google Scholar
  8. Benhamu M, Doytsher Y (2001) Research toward a multilayer 3-D cadastre: interim results. In: International workshop on “3D Cadastres”, registration of properties in strata, Delft, pp 35–51Google Scholar
  9. Benhamu M, Doytsher Y (2003) Toward a spatial 3D cadastre in Israel. Comput Environ Urban Syst 27:359–374CrossRefGoogle Scholar
  10. Biljecki F, Ledoux H, Stoter J (2016) An improved LOD specification for 3D building models. Comput Environ Urban Syst 59:25–37CrossRefGoogle Scholar
  11. Chiang HC (2012) Data modelling and application of 3D cadastre in Taiwan. In: 3rd international workshop on 3D cadastres: developments and practices, Shenzhen, pp 137–157Google Scholar
  12. Clemen C, Gruendig L (2009) 3D building information efficiently acquired and managed. In: Proceedings of the FIG Comissions 5, 6 and SSGA workshop, Lake Baikal, Listvyanka, pp 12–19Google Scholar
  13. Dimopoulou E, Karki S, Miodrag R, Almeida JPD, Griffith-Charles C, Thompson R, Ying S, Oosterom P (2016) Initial registration of 3D parcels. In: 5th International FIG 3D cadastre workshop, Athens, pp 105–132Google Scholar
  14. Donath D, Thurow T (2007) Integrated architectural surveying and planning: methods and tools for recording and adjusting building survey data. Autom Constr 16:19–27CrossRefGoogle Scholar
  15. Eaglin T, Subramanian K, Payton J (2013) 3D modelling by the masses: a mobile app for modelling buildings. In: 2013 IEEE international conference on pervasive computing and communications workshops (PERCOM workshops), pp 315–317Google Scholar
  16. Ellul C, de Almeida JP, Romano R (2016) Does coimbra need a 3d cadastre? Prototyping a crowdsourcing app as a first step to finding out. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci IV-2/W1:55–62.
  17. Enemark S, Bell K, Lemmen C, McLaren R (2014) Building fit-for-purpose land administration systems. In: Proceedings of the XXV FIG congress, Kuala Lumpur. Accessed 18 July 2017
  18. Enemark S, McLaren R, Lemmen C (2015) Fit-for-purpose land administration—guiding principles. UN-HABITAT/GLTN, NairobiGoogle Scholar
  19. ESRI (2017) ArcGIS online application. Accessed 20 Aug 2017
  20. Fan H, Zipf A (2016) Modelling the world in 3D from VGI/Crowdsourced data. In: Capineri C, Haklay M, Huang H, Antoniou V, Kettunen J, Ostermann F, Purves R (eds) European handbook of crowdsourced geographic information. Ubiquity Press, London, pp 435–446CrossRefGoogle Scholar
  21. García JMO, Soriano LIV, Martín-Varés AV (2011) 3D modelling and representation of the spanish cadastral cartography. In: 2nd international workshop on 3D cadastres, Delft, pp 209–222Google Scholar
  22. General Assembly Resolution 70/1 (2015) Transforming our world: the 2030 agenda for sustainable development, A/RES/70/1 (25 September 2015). Accessed Sept 2017
  23. Gkeli M, Apostolopoulos K, Mourafetis G, Ioannidis C, Potsiou C (2016) Crowdsourcing and mobile services for a fit-for-purpose cadastre in Greece. In: Fourth international conference on remote sensing and geoinformation of the environment (RSCy2016), SPIE 9688:17.
  24. Gkeli M, Ioannidis C, Potsiou C (2017a) The potential use of VGI for 3D cadastre surveys. Coord Mag XIII(10):14–19Google Scholar
  25. Gkeli M, Ioannidis C, Potsiou C (2017b) Review of the 3D modelling algorithms and crowdsourcing techniques—an assessment of their potential for 3D cadastre. In: FIG working week 2017—“surveying the world of tomorrow—from digitalisation to augmented reality”, Helsinki, pp 1–23Google Scholar
  26. Gkeli M, Ioannidis C, Potsiou C (2017b) 3D modelling algorithms and crowdsourcing techniques. Coord Mag 13(9):7–14Google Scholar
  27. Goetz M, Zipf A (2012) Towards defining a framework for the automatic derivation of 3D CityGML models from volunteered geographic information. Int J 3-D Inf Model (IJ3DIM) 1(2):1–16. CrossRefGoogle Scholar
  28. Goodchild MF (2007a) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221. CrossRefGoogle Scholar
  29. Goodchild MF (2007b) Citizens as voluntary sensors: spatial data infrastructure in the world of Web 2.0. Int J Spatial Data Infrastruct Res 2:24–32.
  30. Gulliver TFD (2015) Developing a 3D digital cadastral system for New Zealand. Master’s Thesis, University of CanterburyGoogle Scholar
  31. Guo R, Luo F, Zhao Z, He B, Li L, Luo P, Ying Sh (2014) The applications and practices of 3D cadastre. In: 4th international workshop on 3D cadastres, Dubai, pp 299–312Google Scholar
  32. Hadjiprocopis A, Ioannides M, Wenzel K, Rothermel M, Johnsons PS, Fritsch D, Weinlinger G (2014) 4D reconstruction of the past: the image retrieval and 3D model construction pipeline. In: Second international conference on remote sensing and geoinformation of the environment (RSCy2014), International Society for Optics and Photonics 9229:16.
  33. Hartmann W, Havlena M, Schindler K (2016) Towards complete, geo-referenced 3d models from crowd-sourced amateur images. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci III:51–58. CrossRefGoogle Scholar
  34. Havlena M, Schindler K (2014) VocMatch: efficient multiview correspondence for structure from motion. In: Computer vision—ECCV 2014: 13th European conference, Zurich, part III, pp 46–60Google Scholar
  35. Jamali A, Anton F, Rahman AA, Boguslawski P, Gold CM (2015) 3D Indoor building environment reconstruction using calibration of range finder data. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci 2:29–34. CrossRefGoogle Scholar
  36. Jeong J, Kim T (2016) Semi-automatic building models and façade texture mapping from mobile phone images. ISPRS Int Arch Photogram Remote Sens Spatial Inf Sci XLI-B3:613–616. CrossRefGoogle Scholar
  37. Karki S (2013) 3D cadastre implementation issues in Australia. Master’s Thesis, University of Southern QueenslandGoogle Scholar
  38. Keenja E, de Vries W, Bennett R, Laarakker P (2012) Crowd sourcing for land administration: perceptions within Netherlands Kadaster. In: FIG working week 2012, Rome. Accessed 11 June 2017
  39. Leberl F (2010) Time for neo-photogrammetry. GIS Dev 14(2):22–24Google Scholar
  40. Mourafetis G, Apostolopoulos K, Potsiou C, Ioannidis C (2015) Enhancing cadastral survey by facilitating owners’ participation. Surv Rev 47(344):316–324. CrossRefGoogle Scholar
  41. Oldfield J, Oosterom P, Quak W, Veen J, Beetz J (2016) Can data from BIMs be used as input for a 3D Cadastre? In: 5th international FIG 3D cadastre workshop, Athens, pp 199–214Google Scholar
  42. OSM (2016) OpenStreetMap Wiki. Accessed 27 Dec 2016
  43. OSM Buildings (2017) OSM buildings. Accessed 10 Aug 2017
  44. Over M, Schilling A, Neubauer S, Zipf A (2010) Generating web-based 3D city models from OpenStreetMap: the current situation in Germany. Comput Environ Urban Syst (CEUS) 34(6):496–507CrossRefGoogle Scholar
  45. Rautenbach V, Coetzee S, Schiewe J, Coltekin A (2015a) An assessment of visual variables for the cartographic design of 3D informal settlement models. In Proceedings of the 27th international cartographic conference. International Cartographic Association, Rio de JaneiroGoogle Scholar
  46. Rautenbach V, Bevis Y, Coetzee S, Combrinck C (2015b) Evaluating procedural modelling for 3D models of informal settlements in urban design activities. S Afr J Sci 111(11/12):1–10CrossRefGoogle Scholar
  47. Rautenbach V, Coetzee S, Coltekin A (2016) Investigating the use of 3D geovisualizations for urban design in informal settlement upgrading in South Africa. Int Arch Photogramm Remote Sens Spatial Inf Sci 41:425–431CrossRefGoogle Scholar
  48. Rosser J, Morley J, Smith G (2015) Modelling of building interiors with mobile phone sensor data. ISPRS Int J Geo-Inf 4:989–1012CrossRefGoogle Scholar
  49. Sankar A, Seitz S (2012) Capturing indoor scenes with smartphones. In: 25th annual ACM symposium on user interface software and technology (UIST’12), Cambridge, MA, pp 403–412Google Scholar
  50. Sensopia MagicPlan (2018) Accessed 5 Mar 2018
  51. Sketch Up (2018) Accessed 5 Mar 2018
  52. Somogyi A, Barsi A, Molnar B, Lovas T (2016) Crowdsourcing based 3d modelling. Int Arch Photogramm Remote Sens Spatial Inf Sci 41(B5):587–590CrossRefGoogle Scholar
  53. Stoter JE, van Oosterom PJM (2005) Technological aspects of a full 3D cadastral registration. Int J Geogr Inf Sci 19(6):669–696CrossRefGoogle Scholar
  54. Stoter J, van Oosterom P, Ploeger H (2012) The phased 3D cadastre implementation in the Netherlands. In: Van Oosterom P, Guo R, Li L, Ying S, Angsüsser S (eds) Proceedings of the 3rd international workshop on 3D cadastres, Shenzhen, pp 201–218Google Scholar
  55. Stoter J, Ploeger H, Roes R, van der Riet E, Biljecki P, Ledoux H (2016) First 3D cadastral registration of multi-level ownerships rights in the Netherlands. In: Van Oosterom P, Dimopoulou E, Fendel E (eds) Proceedings of 5th international FIG 3D cadastre workshop, Athens, pp 491–504Google Scholar
  56. Uden M, Zipf A (2013) Open building models: towards a platform for crowdsourcing virtual 3D cities. In: Pouliot J, Daniel S, Hubert F, Zamyadi A (eds) Progress and new trends in 3D geoinformation sciences. Springer, Berlin, pp 299–314CrossRefGoogle Scholar
  57. Vandysheva N, Tikhonov V, Van Oosterom P, Stoter J, Ploeger H, Wouters R, Penkov V (2011a) 3D cadastre modelling in Russia. In: Proceedings FIG working week 2011, Marrakech, p 19Google Scholar
  58. Vandysheva N, Ivanov A, Pakhomov S, Spiering B, Stoter J, Zlatanova S, van Oosterom P (2011b) Design of the 3D cadastre model and development of the prototype in the russian federation. In: 2nd International workshop on 3D cadastres, Delft, pp 355–375Google Scholar
  59. Zhang Z, He JN, Huang S, Duan Y (2016) Dense image matching with two steps of expansion. ISPRS Int Arch Photogramm Remote Sens Spatial Inf Sci 41(B3):143–149CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Technical University of AthensAthensGreece

Personalised recommendations